The study aims to correlate the mean gap voltage variation and wire breakage occurrences during the wire EDM of Inconel 718. A novel approach to predict the wire breakage is introduced by considering the mean gap voltage variation (ΔV_m) as an indicator of the instabilities in the spark gap. Such instabilities are regarded as the primary reason for wire breakages and inferior part quality of wire electric discharge machined components. The parameter ΔV_m is a process data obtained as the difference between servo voltage and mean gap voltage (V_m). It was found experimentally that if the value of ΔV_m crosses a threshold limit, the process interruptions through wire breakages were observed. In order to predict the wire breakage situations, the study models ΔV_m using adaptive neuro fuzzy inference system (ANFIS). Based on central composite design (CCD) of response surface methodology (RSM), 31 experiments were conducted and ΔV_m is recorded as the response. The input parameters considered were pulse on time, pulse off time, servo voltage and wire feed rate. The ANFIS model was found very accurate in predicting ΔV_m, based on which wire breakage alerts can be given. The capability of the model is further confirmed by verification experiments. EDS and microstructural analysis further revealed the effect of ΔV_m on wire wear and part quality. Higher value of ΔV_m resulted in greater wire wear and inferior part quality. The surface finish and flatness error of machined parts were measured to compare the part quality.

该研究的目的是因科镍合金718的新方法的电火花线切割中相互关联的平均间隙电压变化和断线的发生，预测通过考虑平均间隙电压变化（ΔV断线引入_米）作为不稳定性的一个指标火花间隙。这种不稳定性被认为是造成断线和电火花线切割加工零件的劣质的主要原因。参数ΔV_米是作为伺服电压和平均间隙电压（V之间的差而获得的处理数据_米）。通过实验发现，如果ΔV值_米超过阈值限制，观察到由于断线而导致的过程中断。为了预测断线情况下，研究模型ΔV_中号使用自适应神经模糊推理系统（ANFIS）。基于响应面分析法（RSM）的中心复合设计（CCD），31个进行了实验和ΔV_米被记录作为响应。考虑的输入参数为脉冲接通时间，脉冲断开时间，伺服电压和送丝速度。该ANFIS模型在预测ΔV发现非常准确_米，基于此断线警报可被给定。通过验证实验进一步确认了模型的功能。EDS和微观结构分析进一步揭示的ΔV的影响_米电线磨损和零件质量。的ΔV值越高_米造成了更大的电线磨损和劣质零件质量。测量加工零件的表面光洁度和平面度误差，以比较零件质量。